Collaborative Research: RoL: Impacts of plants and communities on soil microbial composition and function across phylogenetic scales

合作研究：RoL：植物和群落对跨系统发育尺度的土壤微生物组成和功能的影响

• 批准号: 1935074
• 负责人: Meghan Midgley
• 金额: $ 79.74万
• 依托单位: Morton Arboretum
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935074&HistoricalAwards=false
• 关键词: Collaborative; Research; RoL; Impacts; plants

Soils provide critical ecosystem services that support agriculture, determine nutrient and carbon cycles, maintain clean air and water, and sustain biodiversity. These services are controlled by microorganisms, including bacteria and fungi. Plants can manipulate these microbial communities through the chemicals they release—root exudates—with subsequent consequences for soil functions. Characterizing root exudates and their relationships with other plant traits will improve understanding of plant effects on microbial communities and the ecosystem services that they provide. This project will use laboratory, greenhouse, field, and computational approaches to evaluate the effects of plant root exudates on soil microbial composition and function. The work will be conducted in the context of restored tallgrass American prairies, an endangered ecosystem, and deliver our results directly to land owners and natural resource managers responsible for the creation and maintenance of this landscape. Additionally, the project provides educational opportunities for members of the public ranging from high school students to retired adults and will train early-career scientists in cutting-edge techniques. This project will develop a predictive, mechanistic framework to understand how phylogenetic and functional characteristics of plants and plant communities influence soil microbial communities and the ecosystem functions via root exudates. It will combine exometabolomic plant measurements, sampling in a biodiversity experiment, functional microbial characterizations, and machine learning approaches to forecast microbial community structure and function in a field setting. Exudate characterizations from a taxonomically and functionally diverse pool of plants will greatly advance knowledge of plant biochemistry. The use of exudates in mesocosm incubation and metabolic characterization experiments will demonstrate their direct impacts on microbial community structure and function. The project will also sample soil microbes in a biodiversity experiment and use machine learning computational approaches to build predictive models of plant community impacts on soil microbes. These models will be tested in a field setting using large-scale tallgrass prairie restorations to produce generalizable conclusions that span molecular- to landscape-scales.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

土壤提供关键的生态系统服务，支持农业、决定养分和碳循环、保持清洁的空气和水以及维持生物多样性。这些服务由微生物控制，包括细菌和真菌。植物可以通过它们释放的化学物质（根系分泌物）来控制这些微生物群落，从而影响土壤功能。描述根系分泌物及其与其他植物性状的关系，将有助于了解植物对微生物群落及其提供的生态系统服务的影响。本研究将利用实验室、温室、田间和计算机模拟等方法，研究植物根系分泌物对土壤微生物组成和功能的影响。这项工作将在恢复高草美国大草原的背景下进行，这是一个濒危的生态系统，并将我们的结果直接提供给负责创造和维护这一景观的土地所有者和自然资源管理者。此外，该项目还为从高中生到退休人员的公众提供教育机会，并将培训早期职业科学家掌握尖端技术。该项目将开发一个预测性的机制框架，以了解植物和植物群落的系统发育和功能特征如何通过根系分泌物影响土壤微生物群落和生态系统功能。它将结合联合收割机外代谢植物测量，生物多样性实验中的采样，功能性微生物表征和机器学习方法来预测田间环境中的微生物群落结构和功能。从植物的分类学和功能多样性池的分泌物特性将大大推进植物生物化学的知识。在围隔培养和代谢表征实验中使用渗出物将证明其对微生物群落结构和功能的直接影响。该项目还将在生物多样性实验中对土壤微生物进行采样，并使用机器学习计算方法建立植物群落对土壤微生物影响的预测模型。这些模型将在野外环境中使用大规模的高草草原植被进行测试，以产生可推广的结论，跨越分子到生物学尺度。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。